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monitoring disturbances from both satellites and ground-based sensors. The latter is expected to contribute to the further development of the pylidar repository. This PhD project will be promoted by prof. Kim
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related field. The candidate should have excellent programming skills (e.g., Python), expertise in machine learning and fluency in English (speech and writing). Prior knowledge of neuroscience and/or deep
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LLM, VLM and embodied AI, with specific applications to collaborative interaction with people. You will be supervised by Prof. Tony Belpaeme (www.tonybelpaeme.me) and will be part of a vibrant and
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Python and are fluent using GitHub in a team setting. You have basic working knowledge on fundamental computer vision tasks such as temporal and spatial segmentation as well as stereo vision. You have
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master’s degree in Computer Science, Geodesy, or related discipline Very good programming knowledge, preferably in Python Experience with state-of-the-art machine learning or data science technologies
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good programming knowledge, preferably in Python Experience with state-of-the-art machine learning or data science technologies Experience with remote sensing data is a plus Experience with industry
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conferences participating in the supervision of undergraduate and postgraduate students cooperating with researchers active within the research group and outside The research project is led by Prof. Fuhui Shen
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methods based on exact algorithms and/or (meta)heuristics (in Python, Java, C++, …) You are willing to invest time and effort in improving both your academic English writing and presentation skills, i.e
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scanning in forests; and (3) apply these methods to gain ecological insights in how forest structure is changing through time. This PhD project will be promoted by prof. Kim Calders, co-supervised by dr